scholarly journals Deep learning for polyp recognition in wireless capsule endoscopy images

2017 ◽  
Vol 44 (4) ◽  
pp. 1379-1389 ◽  
Author(s):  
Yixuan Yuan ◽  
Max Q.-H. Meng
Keyword(s):  
Deep Learning ◽  
Capsule Endoscopy ◽  
Wireless Capsule Endoscopy ◽  
Wireless Capsule

scholarly journals Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Sen Wang ◽  
Yuxiang Xing ◽  
Li Zhang ◽  
Hewei Gao ◽  
Hao Zhang
Keyword(s):  
Deep Learning ◽  
Capsule Endoscopy ◽  
Recognition Task ◽  
Wireless Capsule Endoscopy ◽  
Test Accuracy ◽  
Learning Networks ◽  
Deep Convolutional Neural Networks ◽  
Deep Cnn ◽  
Wireless Capsule ◽  
Better Than

Wireless capsule endoscopy (WCE) has developed rapidly over the last several years and now enables physicians to examine the gastrointestinal tract without surgical operation. However, a large number of images must be analyzed to obtain a diagnosis. Deep convolutional neural networks (CNNs) have demonstrated impressive performance in different computer vision tasks. Thus, in this work, we aim to explore the feasibility of deep learning for ulcer recognition and optimize a CNN-based ulcer recognition architecture for WCE images. By analyzing the ulcer recognition task and characteristics of classic deep learning networks, we propose a HAnet architecture that uses ResNet-34 as the base network and fuses hyper features from the shallow layer with deep features in deeper layers to provide final diagnostic decisions. 1,416 independent WCE videos are collected for this study. The overall test accuracy of our HAnet is 92.05%, and its sensitivity and specificity are 91.64% and 92.42%, respectively. According to our comparisons of F1, F2, and ROC-AUC, the proposed method performs better than several off-the-shelf CNN models, including VGG, DenseNet, and Inception-ResNet-v2, and classical machine learning methods with handcrafted features for WCE image classification. Overall, this study demonstrates that recognizing ulcers in WCE images via the deep CNN method is feasible and could help reduce the tedious image reading work of physicians. Moreover, our HAnet architecture tailored for this problem gives a fine choice for the design of network structure.

scholarly journals Prelimenary results of red lesion segmentation in WCE images

2021 ◽  
Vol 297 ◽  
pp. 01060
Author(s):  
Said Charfi ◽  
Mohamed El Ansari ◽  
Ayoub Ellahyani ◽  
Ilyas El Jaafari
Keyword(s):  
Deep Learning ◽  
Capsule Endoscopy ◽  
Imaging Technique ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Lesion Detection ◽  
Gastrointestinal Illness ◽  
Lesion Segmentation ◽  
Entire Small Bowel ◽  
Wireless Capsule

Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has encouraged the researchers to provide automated diagnostic technics for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, we present the prelimenary results of red lesion detection in WCE images using Dense-Unet deep learning segmentation model. To this end, we have used a dataset containing two subsets of anonymized video capsule endoscopy images with annotated red lesions. The first set, used in this work, has 3,295 non-sequential frames and their corresponding annotated masks. The results obtained by the proposed scheme are promising.

Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis

2020 ◽  
Vol 92 (4) ◽  
pp. 831-839.e8 ◽  
Author(s):  
Shelly Soffer ◽  
Eyal Klang ◽  
Orit Shimon ◽  
Noy Nachmias ◽  
Rami Eliakim ◽  
...  
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Capsule Endoscopy ◽  
Meta Analysis ◽  
Wireless Capsule Endoscopy ◽  
Wireless Capsule

scholarly journals Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1265 ◽  
Author(s):  
Haya Alaskar ◽  
Abir Hussain ◽  
Nourah Al-Aseem ◽  
Panos Liatsis ◽  
Dhiya Al-Jumeily
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Capsule Endoscopy ◽  
Wireless Capsule Endoscopy ◽  
Automated System ◽  
Superior Performance ◽  
Learning Networks ◽  
Low Contrast ◽  
Wireless Capsule

Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The medical image datasets used in this study were obtained from WCE video frames. In this work, two milestone CNN architectures, namely the AlexNet and the GoogLeNet are extensively evaluated in object classification into ulcer or non-ulcer. Furthermore, we examine and analyze the images identified as containing ulcer objects to evaluate the efficiency of the utilized CNNs. Extensive experiments show that CNNs deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools.

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